class_docstr: Strategy for training with multiple processes in parallel.
code: def __init__(
      self,
      accelerator: Optional["pl.accelerators.Accelerator"] = None,
      parallel_devices: Optional[List[torch.device]] = None,
      cluster_environment: Optional[ClusterEnvironment] = None,
      checkpoint_io: Optional[CheckpointIO] = None,
      precision_plugin: Optional[Precision] = None,
  ):
      super().__init__(
          accelerator=accelerator,
          checkpoint_io=checkpoint_io,
          precision_plugin=precision_plugin,
      )
      self.parallel_devices = parallel_devices
      self.cluster_environment: Optional[
          ClusterEnvironment
      ] = cluster_environment
      self._layer_sync: Optional[LayerSync] = None
  
imports: from abc import ABC, abstractmethod
  from contextlib import contextmanager
  from typing import Any, Dict, Generator, List, Optional
  import torch
  from torch import Tensor
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.plugins import CheckpointIO, ClusterEnvironment
  from lightning.fabric.utilities.distributed import ReduceOp, _all_gather_ddp_if_available
  from lightning.pytorch.plugins import LayerSync
  from lightning.pytorch.plugins.precision import Precision
  from lightning.pytorch.strategies.strategy import Strategy
label_desc: Use image node_id 1 to create a new ParallelStrategy object from inherited base classes: Strategy, ABC with example: obj = ParallelStrategy(accelerator, parallel_devices, cluster_environment, checkpoint_io, precision_plugin)